The Application of Deep Learning on CBCT in Dentistry

Author:

Fan Wenjie12,Zhang Jiaqi12,Wang Nan12,Li Jia12,Hu Li12ORCID

Affiliation:

1. Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China

2. School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China

Abstract

Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user’s proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.

Funder

Clinical Research Project of the Orthodontic Committee of the Chinese Stomatological Association

Hubei Province Intellectual Property High-Value Cultivation Project

Science and Technology Department of Hubei Province

Publisher

MDPI AG

Subject

Clinical Biochemistry

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